System and method for optimally managing heterogeneous data in a distributed storage environment

ABSTRACT

This technology relates to data management computing apparatuses, methods, and non-transitory computer-readable media that optimally manage heterogeneous data in a distributed environment in real time. The method comprises initializing a first storage pool capable of storing data from one or more sources, the first storage pool being distributed across one or more computing devices. Then data from the one or more sources may be stored in the first storage pool. Subsequently, one or more memory pools may be generated in a second storage pool based on amount of data to be stored in the first storage pool and one or more parameters associated with the data stored in the first storage pool. Finally, metadata is created in a first memory pool of the one or more memory pools for the data stored in the first storage pool, the metadata capable of retrieving the data stored in the first storage pool in real-time.

This application claims the benefit of Indian Patent Application No.4676/CHE/2014 filed Sep. 24, 2014, which is hereby incorporated byreference in its entirety.

FIELD

This technology relates generally to managing data, and moreparticularly but not limited to optimally managing heterogeneous data ina distributed storage environment in real time.

BACKGROUND

In present day scenario, considering the exponential increase in datastorage requirements and drastic reduction in storage cost pergigabytes, aggressive optimization of data written to storage media isnot seriously taken. This leads to generation of extremely largeunmanaged datasets distributed across multiple systems, cloud basedsystems, and various other places. Querying these large datasets entailsefforts as it is not known which data lies where. Further, there are nomechanisms available for creating common index for pulling out data fromextremely large datasets spread across various systems and for handlingsuch data efficiently.

Currently, the data is spread across multiple servers which areinterconnected with each other. Various techniques are being developedto leverage the collective power of all the interconnected servers. Themain problem is how to efficiently make use of data resources spreadacross servers available as a single pool of resources for dataprocessing applications, i.e., how to deal with extremely largedatasets, for example, (archived official datasets for a company, videosurveillance data, web crawled data for search engine) which may be onlyunstructured data and is continuously expanding with time. The mainproblems associated with such kind of data are as follows:

-   -   Lack of proper centralized yet distributed storage space    -   Lack of computing services available for the given data size.    -   No proper access mechanism.    -   Data is unstructured.    -   Access is very low.

In view of the above drawbacks, it would be desirable to have amechanism to use large datasets spread across systems in an efficientand fault tolerant manner in real time.

SUMMARY

Disclosed herein is a method for optimally managing heterogeneous datain a distributed storage environment. The method includes initializing afirst storage pool capable of storing data from one or more sources, thefirst storage pool being distributed across one or more computingdevices; storing data from the one or more sources in the first storagepool; generating one or more memory pools in a second storage pool basedon amount of data to be stored in the first storage pool and one or moreparameters associated with the data stored in the first storage pool;and creating metadata in a first memory pool of the one or more memorypools for the data stored in the first storage pool, the metadatacapable of retrieving the data stored in the first storage pool inreal-time.

In another aspect of this technology, a data management computingapparatus that optimally manages heterogeneous data in a distributedstorage environment is disclosed. The data management computingapparatus includes one or more hardware processors and acomputer-readable medium storing instructions that, when executed by theone or more hardware processors, cause the one or more hardwareprocessors to perform operations. The operations may includeinitializing a first storage pool capable of storing data from one ormore sources, the first storage pool being distributed across one ormore computing devices; storing data from the one or more sources in thefirst storage pool; generating one or more memory pools in a secondstorage pool based on amount of data to be stored in the first storagepool and one or more parameters associated with the data stored in thefirst storage pool, the second storage pool being distributed across theone or more computing devices; and creating metadata in a first memorypool of the one or more memory pools for the data stored in the firststorage pool, the metadata capable of retrieving the data stored in thefirst storage pool in real-time.

In yet another aspect of this technology, a non-transitorycomputer-readable medium storing instructions for optimally managingheterogeneous data in a distributed storage environment that, whenexecuted by the one or more hardware processors, cause the one or morehardware processors to perform operations is disclosed. The operationsmay include initializing a first storage pool capable of storing datafrom one or more sources, the first storage pool being distributedacross one or more computing devices; storing data from one or moresources in the first storage pool; generating one or more memory poolsin a second storage pool based on amount of data to be stored in thefirst storage pool and one or more parameters associated with the datastored in the first storage pool, the second storage pool beingdistributed across the one or more computing devices; and creatingmetadata in a first memory pool of the one or more memory pools for thedata stored in the first storage pool, the metadata capable ofretrieving the data stored in the first storage pool in real-time.

Additional objects and advantages of this technology will be set forthin part in the following detailed description, and in part will beobvious from the description, or may be learned by practice of thepresent disclosure. The objects and advantages of the present disclosurewill be realized and attained by means of the elements and combinationsparticularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which constitute a part of thisspecification, illustrate several embodiments and, together with thedescription, serve to explain the disclosed principles. In the drawings:

FIG. 1 is a block diagram of a network environment with a functionaldiagram of a data management computing apparatus that optimally managesheterogeneous data in real time among a plurality of devices in anetwork, according to some embodiments of the present disclosure.

FIG. 2 is a flowchart of an exemplary method for optimally managingheterogeneous data among the plurality of devices in the network in realtime, according to some embodiments of the present disclosure.

FIG. 3 is a block diagram of an example of the data management computingapparatus for implementing embodiments consistent with the presentdisclosure.

DETAILED DESCRIPTION

As used herein, reference to an element by the indefinite article “a” or“an” does not exclude the possibility that more than one of the elementis present, unless the context requires that there is one and only oneof the elements. The indefinite article “a” or “an” thus usually means“at least one.” The disclosure of numerical ranges should be understoodas referring to each discrete point within the range, inclusive ofendpoints, unless otherwise noted.

As used herein, the terms “comprise,” “comprises,” “comprising,”“includes,” “including,” “has,” “having,” “contains,” or “containing,”or any other variation thereof, are intended to cover a non-exclusiveinclusion. For example, a composition, process, method, article, system,apparatus, etc. that comprises a list of elements is not necessarilylimited to only those elements but may include other elements notexpressly listed. The terms “consist of,” “consists of,” “consistingof,” or any other variation thereof, excludes any element, step, oringredient, etc., not specified. The term “consist essentially of,”“consists essentially of,” “consisting essentially of,” or any othervariation thereof, permits the inclusion of elements, steps, oringredients, etc., not listed to the extent they do not materiallyaffect the basic and novel characteristic(s) of the claimed subjectmatter.

The present disclosure relates to a data management computing apparatusand a method for leveraging a combination of distributed memorymanagement and the distributed data management. A centralized memoryspread across systems is integrated with a persistent storage spreadacross the systems where data can reside efficiently with custom builtalgorithms for efficient access to the data.

The system is based on a centralized and distributed architecture both,where a data index or metadata (location of data, access rights etc.) isplaced centrally in one or more memory pools and actual data is storedlocally across multiple systems, contributing part of the one or morememory pools. In this way, each system knows location of all filespresent while holding a small part of the data. Similar data iscategorized together. This helps the system to browse across similarcategories efficiently. When a user wants a particular data, he onlyneeds to access the metadata which is stored centrally in the one ormore memory pools. Since the metadata has information about the locationof data present across systems, a stream of requested data is redirectedback to the user instantly.

FIG. 1 illustrates a network environment 100 incorporating a datamanagement computing apparatus 102 that optimally manages data in realtime among a plurality of devices 104 in a network 106, according tosome embodiments of this technology.

The data management computing apparatus 102 may be implemented in avariety of computing systems, such as a laptop computer, a desktopcomputer, a notebook, a workstation, a mainframe computer, a server, anetwork server, and the like. Further, as shown in FIG. 1, the pluralityof devices 104-1, 104-2, 104-3, 104-N are communicatively coupled toeach other and to the data management computing apparatus 102 throughthe network 106 for facilitating one or more end users to access and/oroperate the data management computing apparatus 102.

Further, the data management computing apparatus 102 may aggregatephysical memory of the plurality of devices 104-1, 104-2, 104-3, 104-N,collectively referred to as devices 104 and individually referred to asdevice 104, to create a pool of memory resources. Examples of thedevices 104 include, but are not limited to, a desktop computer, aportable computer, a server, a handheld device, and a workstation. Thedevices 104 may be used by various stakeholders or end users, such assystem administrators and application developers. In one implementation,the data management computing apparatus 102 may be configured in atleast one of the device 104 to aggregate the memory of the plurality ofdevices 104.

The network 106 may be a wireless network, wired network or acombination thereof. The network 106 can be implemented as one of thedifferent types of networks, such as intranet, local area network (LAN),wide area network (WAN), the internet, and such. The network 106 mayeither be a dedicated network or a shared network, which represents anassociation of the different types of networks that use a variety ofprotocols, for example, Hypertext Transfer Protocol (HTTP), TransmissionControl Protocol/Internet Protocol (TCP/IP), Wireless ApplicationProtocol (WAP), etc., to communicate with each other. Further, thenetwork 106 may include a variety of network devices, including routers,bridges, servers, computing devices, storage devices, etc.

The data management computing apparatus 102 may include a processor (notshown in FIG. 1), a memory (not shown in FIG. 1) coupled to theprocessor, and interfaces (not shown in FIG. 1) which are illustrated inthe corresponding block diagram of the data management computingapparatus 301 which may implement the functionality illustrated in thefunctional block diagram of the data management computing apparatus 102shown in FIG. 1. The processor may be implemented as one or moremicroprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, logic circuitries,and/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the processor is configured tofetch and execute computer-readable instructions stored in the memory.The memory can include any non-transitory computer-readable medium knownin the art including, for example, volatile memory (e.g., RAM), and/ornon-volatile memory (e.g., EPROM, flash memory, etc.).

The interface(s) may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,etc., allowing the data management computing apparatus 102 to interactwith the devices 104. Further, the interface(s) may enable the datamanagement computing apparatus 102 respectively to communicate withother computing devices. The interface(s) can facilitate multiplecommunications within a wide variety of networks and protocol types,including wired networks, for example LAN, cable, etc., and wirelessnetworks such as WLAN, cellular, or satellite. The interface(s) mayinclude one or more ports for connecting a number of devices to eachother or to another server.

As shown in FIG. 1, the data management computing apparatus 102 mayinclude a centralized memory 108, a storage repository 110, a metadataserialization manager 112, a storage mapping module 114, a dataoperation catalogue 116, a combined sharding engine 118, and a datapassage interface 120. The centralized memory 108 may be volatile randomaccess memory (RAM) whereas the storage repository may be nonvolatilepersistent storage like hard disk drive or solid state drive devices. Inan exemplary embodiment, suppose there are 20 servers, each serverhaving 20 GB of HDD/SDD storage. Each server may contribute 4 GB ofHDD/SDD towards a unified pool of memory resources having a total memoryof 80 GB. The 80 GB memory results out of an aggregation of physicalmemory resources of each of the servers. This aggregation would presentitself as a unified pool of memory resources. Both the centralizedmemory 108 and the storage repository 110 may be spread across thedevices 104 which may be connected to each other and contributing to bea part of the pooled memory resources. A plurality of services run onthe devices 104 to which all the memory resources present in variousdevices 104 are exposed. These services may be used to glue all thememory spread across devices 104 and make it available as single pool ofmemory, for example, the centralized memory 108 for memory-intensiveapplications to run efficiently. If a new device 104 is installed in thenetwork environment 100, the service may be installed in the systemautomatically. The service may expose the memory in the device 104 inwhich it is running. All the exposed memory resources spread across thedevices 104 may be linked. After linking all the memory resourcesexposed by the services, the exposed memory resources may be madeavailable as single pool of unified resources, specifically memory.

The centralized memory 108 may comprise one or more memory poolsgenerated by a communicatively coupled metadata serialization manager112. In an exemplary embodiment, the one or more generated memory poolsmay be metadata memory pool 124 and cached data store memory pool 126.

Each device 104 contributing to be the part of centralized memory 108has an underlying persistent storage space whose storage capacity isusually more than 100× larger than the memory contributing to be part ofthe centralized memory 108. This storage may be used for high volumedata repository 110 handling heterogeneous varieties of data spreadacross persistent storage in the devices 104. The details of each filestored in the distributed repository 110 may be updated in the metadatastored in the metadata memory pool 124. The updating of the metadatahappens by collecting details from the storage mapping module 114 by themetadata serialization manager 112.

The metadata serialization manager 112 may accept details of the datastored in persistent storage and updates the metadata stored in thecentralized memory 108. After getting the updates, it may access datamaps from the storage mapping module 114 for each file which is thenserialized and converts it into memory objects for in-memory access.Data maps may be combined details of each file stored in the storagerepository 110. The metadata serialized objects may be also written infile system in the storage repository 110 redundantly in defined timeintervals for recovering from failures. The metadata details may becaptured from the storage mapping module 114. The details may includefollowing parameters which can increase over time:—Location of actualdata files spread across multiple devices 104, file permission details,concurrent access rights and exclusive access details, data criticality(no. of data copies to maintain across the devices 104 for faulttolerance), data shard details, file status (Deleted, Writing inprogress, streaming data, file cached in the cached data store memorypool or not or pending for deletion etc.)

The storage mapping module 114 may be designed for taking updates fromthe data operation catalogue module 116, the storage repository 110, andcentralized memory 108 to create an overall map of data stored inpersistent storage in the storage repository 110. The created mapcontains details captured from all the modules it is interacting with.For example It has index of all the operations performed on the givendata set with the help of data operation catalogue module 116, it hasthe current location and details about all the copies of the given dataset using storage repository 110 and also when the given data set iscached in cached data store memory pool 126 the same is updated in themap. This map may be forwarded to the metadata serialization manager 112for updating the overall metadata status in the metadata memory pool.The caching status of each file may be available with the storagemapping module 114 since it has direct access to the storage repository110 and cached data store memory pool 126.

Further, the data passage interface 120 may be single point of contactin the data management computing apparatus 102 where an end user caninteract with the data management computing apparatus 102. The datapassage interface 120 may be responsible for handling all the input andoutput operations happening in the data management computing apparatus102. Whenever there is a read request for any file stored in the system,it is catered via the data passage interface 120 which interacts withmetadata stored in the metadata memory pool 124 using the metadataserialization framework 112 and the storage mapping module 114. Despitethe data is present in cached data store memory pool 126, same may beserved to the data passage interface 120 using the storage mappingmodule 114. This interface may also provide frequency of access of eachfile to the storage mapping module 114 which helps in caching frequentlyaccessed data from the cached data store memory pool 126 in thecentralized memory 108 for even faster access.

All tasks related to data writing in the distributed storage layer inthe storage repository 110 may be handled by the combined shardingmodule 118. This module has an intelligent sharding engine which maydetect the type of file and categorize them accordingly. A user may alsospecify a custom type which can help him/her to categorize filesaccording to his need. If a file type is not detected by this module ora user has enforced no file type detection then the file is directlystored in one of the available persistent storage with ample space. Thesharding details and location of file in persistent storage may beforwarded to the data operation catalogue module 116 which may help inupdating the storage mapping module 114 and the overall metadata of thestorage repository 110.

A real-time data repository 110 should provide enough performance whichqualifies to be real-time and still provide most of the I/O operationson its data. The data operation catalogue module 116 may be responsiblefor handling and processing all the JO operations performed in thesystem. The data operation catalogue module 116 may majorly provideupload, download, delete and append operations. Operations are notlimited by these four types and can extend over time.

When a user uploads a file in the data passage interface 120, it may berouted via the combined sharding module 118 which decides when to placethe file according to the shard hints given or the file type uploaded. Auser may also specify the data criticality of the file so that the datamanagement computing apparatus 102 may redundantly place multiple copiesof same file in the storage repository 110 for fault tolerance. Afterthe file upload is completed the same information (including thelocation of multiple copies) may be updated in the metadata memory pool124 using the storage mapping module 114 and the metadata serializationmanager 112.

For handling a file request operation, the data passage interface 120may interact with the storage mapping module (after getting storagelocation from the metadata serialization manager 112) for nearest copyof requested data which can be served to the requester.

When an update/append or delete operation is requested in the datapassage interface 120, same operation may be repeated for finding thenearest location of data using the storage mapping module 114. Here auser may specify whether to retain old copy of data or not. Even if theuser has requested not to keep an old copy, still the storage repository110 may retain an older version of the data based on the free spaceavailable in the storage repository 110 and a configured amount of timedefined in the storage repository 110.

The data operation catalogue module 116 may include a data access indexcreator 128 and a metadata integrator 130. The data management computingapparatus 102 may handle multiple users at the same time for accessingsame data. Accessing same data may cause corruption when more than oneuser is updating the same file. For avoiding this problem the storagerepository 110 has a connection to centralized memory pools which has anAPI module exposed to cater the access mechanism for each file stored inthe storage repository 110. It uses concept of Semaphore and Mutualexclusion where multiple users can effectively access files concurrentlybased on the status of semaphore and mutex index. In an exemplaryembodiment, basically if a file has a semaphore value of five then therepository would allow five concurrent users to access same file withoutcorruption. Mutual exclusion provides an exclusive access to file whichonly one user can access. Mutex is mostly required for update operationson the stored data.

The work of the metadata integrator may be to aggregate access detailsabout each file stored in the storage repository 110 and update the samein metadata using storage mapping module 114 and the metadataserialization manager 112. To avoid data corruption by allowing morethan defined access to a single file, the metadata integrator mayactively updates the status of each file stored in the system sinceusers may access files continuously and access index can increase anddecrease frequently.

FIG. 2 is a flowchart of an example of a method for optimally managingdata among a plurality of devices 104 in a network 106 in real timeaccording to some embodiments of this technology. The method may beexecuted by the system 100 as described in further detail below. It isnoted however, the functions and/or steps of FIG. 2 as implemented bydata management computing apparatus 102 may be provided by differentarchitectures and/or implementations without departing from the scope ofthis technology.

Referring to FIG. 2, at step 200, identify the heterogeneous datasources and initialize the storage repository 110 in real time. In thisinitializing step, once the sources, i.e., the devices 104 have beenidentified, the instructions may be given to the data managementcomputing apparatus 102 for initializing the storage repository 110. Theinitialization starts all the required services on the devices 104 andprovides information parameters of the centralized memory 108 and totalpersistent storage, i.e. the storage repository 110. After the serviceshave been initialized and capacity details are circulated across theservers, the storage repository 110 may be made available for use.

At step 202, create dynamic memory pools based on the requirements ofthe storage repository 110 in real time. Once the storage repository 110is available for use, the data loading may be started after creating oneor more memory pools in the centralized memory 108. As discussedearlier, the one or more memory pools may comprise metadata memory pool124 and cached data store memory pool 126. The metadata memory pool 124and cached data store memory pool 126 are defined as follows:

Meta Data memory pool—The storage repository 110 is serving requests inreal-time with instant access to any file requested among a largecollection of heterogeneous files. This is achieved by variousmechanisms built in the storage repository 110 like caching mechanism,in-memory access to file location, dedicated metadata memory pools oflarge size for proper working and access to the persistent storage etc.All these mechanism details are accessed in fraction of seconds forserving real-time requests by storing them in the centralized memory 108in form of metadata. The metadata are the serialized memory objectswhich hold the details of each file stored in the storage repository110, location of nearest copy, state of file, no of users connected,cached file details, no of memory pools used etc.

Cached Data Store memory pool—Cached Data Store memory pool is cachestorage for files which are frequently accessed by the data managementcomputing apparatus 102. This may be required to reduce overall load onthe storage repository 110 only. After a defined frequency of access,the data passage interface 120 may instruct the storage mapping module114 to cache a given file for even faster access. The status of file maybe then updated in the metadata memory pool 124 and the next access tothe same file may be served from the cache data store memory pool 126without bothering the storage repository only.

The number and size of these memory pools are based on the amount ofdata to be loaded in the system and various other parameters like futurememory requirements, data access policies, data redundancy details etc.One of the Memory pool, i.e. metadata memory pool 124 is dedicated forhandling the metadata for all the data stored in the storage repository110. This metadata has all the details about the data stored. Themetadata includes the location of the data, access policies, number ofcopies, status of the data, retention period etc. Also it has thedetails about the nearest copy of the data across the devices 104 toserve in lowest time possible. All the operations performed in thestorage repository 110 are handled by the APIs provided.

At step 204, create and maintain the metadata memory pool. The storagerepository 110 may be serving heterogeneous data in real-time. Thisspeed may be maintained with the help of various memory pools present inthe centralized memory 108. One of the memory pools, i.e., metadatamemory pool 124 handles the metadata of all the data in the stored inthe storage repository 110.

All the metadata details which are stored in a dedicated metadata memorypool 124 may act as the contact point for the files stored in thestorage repository 110. The information stored with metadata is verycritical and the storage repository 110 cannot afford to lose thisinformation. Thus this metadata which is present in the metadata memorypool 124 may be redundantly stored in the storage repository 110 whichcomprises the persistent storage. Persistent Storage may be the hardwarestorage (HDD/SSD etc) devices present in all the devices 104 for storingdata. Also whenever this storage repository 110 is restarted, instead ofcreating the metadata index again it may directly read this informationflushed to the persistent storage and validates against it for anychanges. This saves overall time and makes it more efficient.

Further, devices 104 may fail from time to time which is collectivelythe part of heterogeneous storage repository 110. This will result inoverall change in the available resources, specifically the overallavailable resources will reduce by the amount these failed systems wereproviding to the repository. Whenever there may be a change in theconfiguration of a system when failure happens, the update may bedistributed instantly to all other devices 104 to update themaccordingly. Thus the current state of the storage repository 110 may bealways transparent to the user to avoid any unknown failures, datacorruption or data loss.

At step 206, perform the file operation processes and update thecorresponding metadata. One of the operation is uploading fileoperation. Whenever a file is uploaded in the storage repository 110,based on the available space the file is stored in the appropriatelocation. During upload only the user has to specify the criticality ofthe file so that the storage repository can place multiple copies of thegiven file across the devices 104. The details are then updated tometadata.

Another operation is downloading file operation. When a file download isinitiated, the data management computing apparatus 102 may automaticallyidentify the nearest copy of the requested file across the devices 104using the in-memory metadata. It may then redirects the requester tothat file copy.

Yet another operation is deleting/updating the operation. When a file isdeleted or updated in the real-time file system, the deleted or olderversion of file is maintained at a separate location and the same isupdated in in-memory metadata. The retention period of the deleted datacan be configured in the repository.

It is to be noted that present disclosure is not limited to above statedfour operations. There may be other operations also.

At step 208, configure distributed access of data sources usingsemaphores and mutex API. The files stored in the storage repository 110may be configured to be accessed in real-time. Also, there may be arequirement when a single file stored in the persistent storage may beaccessed by multiple users of the storage repository 110. This kind ofaccess is fine when there is only a read request by the users. But incases where multiple users are accessing it for read and writeoperations, the data might get corrupted. To handle such situations anAPIs module may be provided to configure distributed access inpersistent storage also. The distributed access uses the concept ofsemaphores and mutual exclusion for defining access. One a user acquireda mutex lock over a file it can't be access by any other users and await flag is given to them. In situations where multiple accesses may beprovided, semaphores are used. The number of semaphores defines thenumber of users. For example, when a user has got a semaphore access thetotal count of semaphore is reduced by one thus the number ofsimultaneous access is also reduced. When the operation is finishedagain the semaphore index is increased by one. All the distributedrelated details are accessed and updated using metadata access.

At step 210, rectify the errors arising using the metadata. In case ofan error arising out of failure of one or two devices 104, theinformation is quickly circulated to the metadata memory pool 124 whichthen initiates the creation of new copies whose redundancy has decreasedafter the system failure. Also the overall state and space available inthe storage repository is updated in the metadata.

Exemplary Computer System

FIG. 3 is a block diagram of an exemplary data management computingapparatus 301 for implementing embodiments consistent with thistechnology including by way of example the data management computingapparatus 102 shown in FIG. 1. Variations of data management computingapparatus 301 may be used for implementing any of the devices and/ordevice components presented in this disclosure, including datamanagement computing apparatus 102. Data management computing apparatus301 may comprise a central processing unit (CPU or processor) 302.Processor 302 may comprise at least one data processor for executingprogram components for executing user- or system-generated requests. Auser may include a person using a device such as such as those includedin this disclosure or such a device itself. The processor may includespecialized processing units such as integrated system (bus)controllers, memory management control units, floating point units,graphics processing units, digital signal processing units, etc. Theprocessor may include a microprocessor, such as AMD Athlon, Duron orOpteron, ARM's application, embedded or secure processors, IBM PowerPC,Intel's Core, Itanium, Xeon, Celeron or other line of processors, etc.The processor 302 may be implemented using mainframe, distributedprocessor, multi-core, parallel, grid, or other architectures. Someembodiments may utilize embedded technologies like application-specificintegrated circuits (ASICs), digital signal processors (DSPs), FieldProgrammable Gate Arrays (FPGAs), etc.

Processor 302 may be disposed in communication with one or moreinput/output (I/O) devices via I/O interface 303. The I/O interface 303may employ communication protocols/methods such as, without limitation,audio, analog, digital, monaural, RCA, stereo, IEEE-1394, serial bus,universal serial bus (USB), infrared, PS/2, BNC, coaxial, component,composite, digital visual interface (DVI), high-definition multimediainterface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n/b/g/n/x,Bluetooth, cellular (e.g., code-division multiple access (CDMA),high-speed packet access (HSPA+), global system for mobilecommunications (GSM), long-term evolution (LTE), WiMax, or the like),etc.

Using the I/O interface 303, the data management computing apparatus 301may communicate with one or more I/O devices. For example, the inputdevice 304 may be an antenna, keyboard, mouse, joystick, (infrared)remote control, camera, card reader, fax machine, dongle, biometricreader, microphone, touch screen, touchpad, trackball, sensor (e.g.,accelerometer, light sensor, GPS, gyroscope, proximity sensor, or thelike), stylus, scanner, storage device, transceiver, videodevice/source, visors, etc. Output device 305 may be a printer, faxmachine, video display (e.g., cathode ray tube (CRT), liquid crystaldisplay (LCD), light-emitting diode (LED), plasma, or the like), audiospeaker, etc. In some embodiments, a transceiver 306 may be disposed inconnection with the processor 302. The transceiver may facilitatevarious types of wireless transmission or reception. For example, thetransceiver may include an antenna operatively connected to atransceiver chip (e.g., Texas Instruments WiLink WL1283, BroadcomBCM4750IUB8, Infineon Technologies X-Gold 518-PMB9800, or the like),providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system(GPS), 2G/3G HSDPA/HSUPA communications, etc.

In some embodiments, the processor 302 may be disposed in communicationwith a communication network 308 via a network interface 307. Thenetwork interface 307 may communicate with the communication network308. The network interface may employ connection protocols including,without limitation, direct connect, Ethernet (e.g., twisted pair10/100/1000 Base T), transmission control protocol/internet protocol(TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communicationnetwork 308 may include, without limitation, a direct interconnection,local area network (LAN), wide area network (WAN), wireless network(e.g., using Wireless Application Protocol), the Internet, etc. Usingthe network interface 307 and the communication network 308, the datamanagement computing apparatus 301 may communicate with devices 309.These devices may include, without limitation, personal computer(s),server(s), fax machines, printers, scanners, various mobile devices suchas cellular telephones, smartphones (e.g., Apple iPhone, Blackberry,Android-based phones, etc.), tablet computers, eBook readers (AmazonKindle, Nook, etc.), laptop computers, notebooks, gaming consoles(Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. Insome embodiments, the data management computing apparatus 301 may itselfembody one or more of these devices.

In some embodiments, the processor 302 may be disposed in communicationwith one or more memory devices (e.g., RAM 313, ROM 314, etc.) via astorage interface 312. The storage interface may connect to memorydevices including, without limitation, memory drives, removable discdrives, etc., employing connection protocols such as serial advancedtechnology attachment (SATA), integrated drive electronics (IDE),IEEE-1394, universal serial bus (USB), fiber channel, small computersystems interface (SCSI), etc. The memory drives may further include adrum, magnetic disc drive, magneto-optical drive, optical drive,redundant array of independent discs (RAID), solid-state memory devices,solid-state drives, etc.

The memory devices may store a collection of program or databasecomponents, including, without limitation, an operating system 316, userinterface application 317, web browser 318, mail server 319, mail client320, user/application data 321 (e.g., any data variables or data recordsdiscussed in this disclosure), etc. The operating system 316 mayfacilitate resource management and operation of the data managementcomputing apparatus 301. Examples of operating systems include, withoutlimitation, Apple Macintosh OS X, Unix, Unix-like system distributions(e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD,etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBMOS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, GoogleAndroid, Blackberry OS, or the like. User interface 317 may facilitatedisplay, execution, interaction, manipulation, or operation of programcomponents through textual or graphical facilities. For example, userinterfaces may provide computer interaction interface elements on adisplay system operatively connected to the data management computingapparatus 301, such as cursors, icons, check boxes, menus, scrollers,windows, widgets, etc. Graphical user interfaces (GUIs) may be employed,including, without limitation, Apple Macintosh operating systems' Aqua,IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows,web interface libraries (e.g., ActiveX, Java, Javascript, AJAX, HTML,Adobe Flash, etc.), or the like.

In some embodiments, the data management computing apparatus 301 mayimplement a web browser 318 stored program component. The web browsermay be a hypertext viewing application, such as Microsoft InternetExplorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure webbrowsing may be provided using HTTPS (secure hypertext transportprotocol), secure sockets layer (SSL), Transport Layer Security (TLS),etc. Web browsers may utilize facilities such as AJAX, DHTML, AdobeFlash, JavaScript, Java, application programming interfaces (APIs), etc.In some embodiments, the data management computing apparatus 301 mayimplement a mail server 319 stored program component. The mail servermay be an Internet mail server such as Microsoft Exchange, or the like.The mail server may utilize facilities such as ASP, ActiveX, ANSIC++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP,Python, WebObjects, etc. The mail server may utilize communicationprotocols such as internet message access protocol (IMAP), messagingapplication programming interface (MAPI), Microsoft Exchange, postoffice protocol (POP), simple mail transfer protocol (SMTP), or thelike. In some embodiments, the data management computing apparatus 301may implement a mail client 320 stored program component. The mailclient may be a mail viewing application, such as Apple Mail, MicrosoftEntourage, Microsoft Outlook, Mozilla Thunderbird, etc.

In some embodiments, data management computing apparatus 301 may storeuser/application data 321, such as the data, variables, records, etc. asdescribed in this disclosure. Such databases may be implemented asfault-tolerant, relational, scalable, secure databases such as Oracle orSybase. Alternatively, such databases may be implemented usingstandardized data structures, such as an array, hash, linked list,struct, structured text file (e.g., XML), table, or as object-orienteddatabases (e.g., using ObjectStore, Poet, Zope, etc.). Such databasesmay be consolidated or distributed, sometimes among the various computersystems discussed above in this disclosure. It is to be understood thatthe structure and operation of the any computer or database componentmay be combined, consolidated, or distributed in any workingcombination.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with this technology. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A method for optimally managing data in adistributed storage environment, the method comprising: initializing, bya data management computing apparatus, a first storage pool capable ofstoring data from one or more sources, the first storage pool beingdistributed across one or more computing devices; storing, by the datamanagement computing apparatus, data from the one or more sources in thefirst storage pool; generating, by the data management computingapparatus, one or more memory pools in a second storage pool based onamount of data to be stored in the first storage pool and one or moreparameters associated with the data stored in the first storage pool;creating, by the data management computing apparatus, metadata in afirst memory pool of the one or more memory pools for the data stored inthe first storage pool, the metadata capable of retrieving the datastored in the first storage pool in real-time; identifying, by the datamanagement computing apparatus, a failure in at least one of the one ormore computing devices; circulating, by the data management computingapparatus, the information regarding the failure to the metadata storedin the second storage pool; creating, by the data management computingapparatus, one or more redundant copies of the data corrupted by thefailure; and updating, by the data management computing apparatus, themetadata in response to creating one or more redundant copies of thedata.
 2. The method of claim 1, wherein the metadata comprises at leastone of location of the data, access rights associated with the data,number of copies of the data, status of the data, retention period ofthe data, location of the nearest copy of the data across the one ormore computing devices.
 3. The method of claim 1, further comprising:performing, by the data management computing apparatus, one or moreoperations associated with the data stored in the first storage pool;and updating, by the data management computing apparatus, the metadatain the second storage pool in response to performing the one or moreoperations associated with the data stored in the first storage pool. 4.The method of claim 1, further comprising; receiving, by the datamanagement computing apparatus, a request to retrieve a first data fromthe first storage pool; fetching, by the data management computingapparatus, a map indicative of the location of the first data in thefirst storage pool; and fetching, by the data management computingapparatus, a nearest copy of the first data using the map.
 5. The methodof claim 1, wherein initializing the first storage pool furthercomprises: initiating, by the data management computing apparatus, oneor more services on the one or more computing devices to provide storagecapacity of the first storage pool; sharing, by the data managementcomputing apparatus, the storage capacity by the one or more computingdevices among themselves.
 6. The method of claim 1, further comprisinggenerating, by the data management computing apparatus, a cache datastore in a second memory pool of the one or more memory pools, the cachedata store capable of storing at least a portion of the data, theportion of the data being frequently accessed data.
 7. The method ofclaim 1, wherein the first storage pool comprises a persistent storageand wherein the second storage pool comprises a volatile random accessmemory.
 8. The method of claim 1, further comprising: providing, by thedata management computing apparatus, concurrent and exclusive access tomultiple users of the first storage pool in real-time using semaphoresand Mutex index.
 9. The method of claim 1, further comprising: grouping,by the data management computing apparatus, similar type of datatogether for faster access.
 10. A data management computing apparatuscomprising: a processor; and a memory coupled to the processor, which isconfigured for executing programmed instructions comprising and storedin the memory to: initialize a first storage pool capable of storingdata from one or more sources, the first storage pool being distributedacross one or more computing devices; store data from the one or moresources in the first storage pool; generate one or more memory pools ina second storage pool based on amount of data to be stored in the firststorage pool and one or more parameters associated with the data storedin the first storage pool, the second storage pool being distributedacross the one or more computing devices; create metadata in a firstmemory pool of the one or more memory pools for the data stored in thefirst storage pool, the metadata capable of retrieving the data storedin the first storage pool in real-time; identify a failure in at leastone of the one or more computing devices; circulate the informationregarding the failure to the metadata stored in the first storage pool;create one or more redundant copies of the data corrupted by thefailure; and update the metadata in response to creating one or moreredundant copies of the data.
 11. The apparatus of claim 10, wherein themetadata comprises at least one of a location of the data, access rightsassociated with the data, number of copies of the data, status of thedata, retention period of the data, location of the nearest copy of thedata across the one or more computing devices.
 12. The apparatus ofclaim 10, wherein the processor coupled to the memory is furtherconfigured to be capable of executing at least one additional programmedinstruction further comprising and stored in the memory to: perform oneor more operations associated with the data stored in the first storagepool; and update the metadata in the second storage pool in response toperforming the one or more operations associated with the data stored inthe first storage pool.
 13. The apparatus of claim 10, wherein theprocessor coupled to the memory is further configured to be capable ofexecuting at least one additional programmed instruction furthercomprising and stored in the memory to: receive a request to retrieve afirst data from the first storage pool; fetch a map indicative of thelocation of the first data in the first storage pool; and fetch anearest copy of the first data using the map.
 14. The apparatus of claim10, wherein the processor coupled to the memory is further configured tobe capable of executing for the initializing the first storage pool atleast one additional programmed instruction further comprising andstored in the memory to: initiate one or more services on the one ormore computing devices to provide storage capacity of the first storagepool; share the storage capacity by the one or more computing devicesamong themselves.
 15. The apparatus of claim 10, wherein the processorcoupled to the memory is further configured to be capable of executingat least one additional programmed instruction further comprising andstored in the memory to: generate a cache data store in a second memorypool of the one or more memory pools, the cache data store capable ofstoring at least a portion of the data, the portion of the data beingfrequently accessed data.
 16. The apparatus of claim 10, wherein thefirst storage pool comprises a volatile random access memory and whereinthe second storage pool comprises a persistent storage.
 17. Anon-transitory computer readable medium having stored thereoninstructions for optimally managing data in a distributed storageenvironment comprising executable code which when executed by aprocessor, causes the processor to perform steps comprising:initializing a first storage pool capable of storing data from one ormore sources, the first storage pool being distributed across one ormore computing devices; storing data from one or more sources in thefirst storage pool; generating one or more memory pools in a secondstorage pool based on amount of data to be stored in the first storagepool and one or more parameters associated with the data stored in thefirst storage pool, the second storage pool being distributed across theone or more computing devices; creating metadata in a first memory poolof the one or more memory pools for the data stored in the first storagepool, the metadata capable of retrieving the data stored in the firststorage pool in real-time; identifying a failure in at least one of theone or more computing devices; circulating the information regarding thefailure to the metadata stored in the first storage pool; creating oneor more redundant copies of the data corrupted by the failure; andupdating the metadata in response to creating one or more redundantcopies of the data.